EP2514155B1 - Hybrid channel estimation using correlation techniques and least squares - Google Patents

Hybrid channel estimation using correlation techniques and least squares Download PDF

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Publication number
EP2514155B1
EP2514155B1 EP10810977.8A EP10810977A EP2514155B1 EP 2514155 B1 EP2514155 B1 EP 2514155B1 EP 10810977 A EP10810977 A EP 10810977A EP 2514155 B1 EP2514155 B1 EP 2514155B1
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EP
European Patent Office
Prior art keywords
signal
noise
correlation
channel
whiten
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EP10810977.8A
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German (de)
English (en)
French (fr)
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EP2514155A2 (en
Inventor
Dayong Chen
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Telefonaktiebolaget LM Ericsson AB
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Telefonaktiebolaget LM Ericsson AB
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00Baseband systems
    • H04L25/02Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202Channel estimation
    • H04L25/0212Channel estimation of impulse response
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00Baseband systems
    • H04L25/02Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202Channel estimation
    • H04L25/0224Channel estimation using sounding signals
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00Baseband systems
    • H04L25/02Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202Channel estimation
    • H04L25/024Channel estimation channel estimation algorithms
    • H04L25/025Channel estimation channel estimation algorithms using least-mean-square [LMS] method
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00Baseband systems
    • H04L25/02Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006Arrangements for removing intersymbol interference
    • H04L25/03178Arrangements involving sequence estimation techniques
    • H04L25/03248Arrangements for operating in conjunction with other apparatus
    • H04L25/03299Arrangements for operating in conjunction with other apparatus with noise-whitening circuitry
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00Baseband systems
    • H04L25/02Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03993Noise whitening

Definitions

  • the present invention relates generally to wireless communication networks, and in particular to a system and method of channel estimation utilizing both correlation and least squares computational approaches.
  • Wireless communication systems modulate data onto electromagnetic carriers, and transmit the data from one or more transmit antennas, across an air interface, to one or more receiver antennas. Processing circuits and software at the receiver then attempt to recover the data from the received signal, which includes the data, interference, and noise. By estimating and suppressing interference and noise, the data may be recovered more accurately. Interference cancellation is thus a ubiquitous feature of wireless communication systems radio receivers, both in fixed network transmission sites (variously known as base stations, Node B, or Access Points) and mobile User Equipment (UE, also known as mobile stations). To accurately estimate (and hence cancel) interference, estimates of the channel must first be formulated which is well modeled as a time varying FIR filter with L+1 taps. Channel estimation in an interference cancellation receiver is a challenging task due to possibly very low operating carrier to interference ratio (C/I).
  • C/I carrier to interference ratio
  • Correlation channel estimation correlates a known training sequence in the received signal to its known values, multiplying with the complex conjugate at different offsets and the correlation peak values are the channel estimates.
  • Least squares channel estimation minimizes the sum of the squared error quantities of the difference between the received signal and the predicted signal which is the known training sequence symbols passing through the FIR filter.
  • a channel estimator can be characterized as either biased or unbiased.
  • An estimator is biased if its statistical expected value is not equal to the true value being estimated.
  • the estimator is unbiased if its expected value is the true value being estimated.
  • whether an estimator is biased or unbiased also depends on the statistics of channel impairment, which includes white noise and interference.
  • An impairment is referred to as "white” if it has a substantially uniform spectral power density - that it, it exhibits a flat frequency spectrum, with equal power in any bandwidth.
  • impairment is referred to as "colored” if it has a non-uniform spectral power density. If impairment is white, the least squares channel estimate is unbiased whereas the correlation channel estimate is biased.
  • a receiver To cancel interference, a receiver must also estimate a spatial-temporal whitening filter.
  • a spatial-temporal whitening filter There are two different approaches. In Spatial-Temporal Interference Rejection Combining (ST-IRC) and indirect Generalized Least Squares (iGLS), the radio channel and the whitening filter are estimated jointly. Alternatively, in iterative channel estimation, the channel is estimated first, and the channel estimates are then used to estimate the whitening filter, as described for example in the patent US 2005/078777 A1 .
  • ST-IRC Spatial-Temporal Interference Rejection Combining
  • iGLS indirect Generalized Least Squares
  • Joint channel estimation has better performance than iterative channel estimation since interference cancellation is part of the channel estimation.
  • the drawback is the high computational complexity. In joint estimation, many parameters are estimated simultaneously, which requires the inversion of large matrices. This is computationally difficult to implement using available processors, such as 16-bit fixed-point DSP devices.
  • iterative channel estimation is much less computationally demanding, and hence can more easily and inexpensively be implemented.
  • iterative channel estimation only small matrices need to be inverted, e.g., a 2x2 compared to a 13x13 matrix for iGLS.
  • the receiver since the initial channel estimation is done without interference cancellation, the receiver must re-estimate the channel after the interference cancellation or whitening.
  • iterative channel estimation is used in both network base stations and mobile UEs. Both solutions use spatial-temporal whitening for interference cancellation using the Whittle-Wiggins-Robinson Algorithm (WWRA).
  • WWRA Whittle-Wiggins-Robinson Algorithm
  • the iterative channel estimation in UE is correlation based, as this method yields better performance in low C/I conditions. At high C/I, the correlation channel estimate is biased, which causes an irreducible bit error floor. Without error correction coding, data throughput will drop significantly due to increased packet retransmission.
  • the iterative channel estimation in base stations is often least squared based, which yields better performance with high C/I.
  • a hybrid channel estimator for a wireless communication system receiver includes both a correlation based channel estimator and a least squares based channel estimator.
  • the correlation based channel estimator is used when signal quality is low or noise is colored.
  • the least squares based channel estimator is used when signal quality is high or noise is white.
  • an interference suppression filter improves signal quality by suppressing interference in a received signal.
  • correlation based channel estimation is performed initially, when signal quality is low, and interference suppression filtering is performed to increase signal quality. These may be done in a number of iterations until the signal quality is improved beyond a predetermined level, when the least squares based channel estimation can be advantageously performed.
  • the training sequence and noise may be whitened prior to performing least squares based channel estimation, which is the final operation before channel estimates are forwarded to a demodulator for coherent demodulation.
  • a method of hybrid channel estimation in a wireless communication receiver as set out in Claim 1.
  • a hybrid channel estimator for a wireless communication receiver as set out in Claim 8.
  • Correlation channel estimation is based on cross-correlations C( j ) between the complex-valued receive signal x ( n ) and the known training sequence symbols t ( k ), k - 0,1,. ..N - 1 where j is a sample index in a search window and N is the number of training symbols.
  • the impairment is modeled as a vector auto-regressive (VAR) process of order K from which the Yule-Walker equation is derived and solved using the efficient WWRA algorithm to obtain the filter B .
  • B ( k ) are either 4x4 or 2x2 real matrices depending on whether the received signal is 2x over-sampled.
  • B B 0 , B 1 ... B K
  • the signal whitening is done by convolving B with the receive signal r ( n ) .
  • FIG. 1 depicts the relevant portion of a wireless communication receiver 10, which may be deployed in a base station or UE. In either case, the receiver 10 includes many additional functional modules not depicted in Figure 1 for clarity.
  • the receiver 10 receives radio signals at one or more antennas 11, which are processed in a receiver chain comprising a front end circuit 12, a hybrid channel estimator of 14, a demodulator 16, and a channel decoder 18.
  • the received signal is low-noise amplified, down-converted to baseband, digitized, and filtered to symbol- or half-symbol spaced samples which are used as input to the hybrid channel estimator 14.
  • the hybrid channel estimator 14 employs both correlation based and least squares based channel estimation techniques, in response to noise properties of the received signal, to most efficiently and accurately generate the channel estimates.
  • the demodulator 16 estimates soft values for the transmitted bits, which in turn are used for channel decoding by the decoder 18.
  • the hybrid channel estimator 14 is applicable in multi-antenna receivers 10 with multiple radio front end circuits 12 producing multiple complex-valued received signals.
  • a method 20 of hybrid channel estimation performed by the hybrid channel estimator 14 is depicted in Figure 2 .
  • the hybrid channel estimator 14 receives the complex signal x ( n ) from the radio front end circuit 12.
  • the number of channel taps to be estimated L + 1 is determined (block 22) by calculating cross-correlations between received signal and know training sequence with different hypothesis of L , comparing the correlation energies, and selecting the L + 1 with the larger energy.
  • Noise metrics are then assessed to determine whether to whiten the signal.
  • is a pre-defined threshold
  • det( Q j 0 ) and tr ( P j 0 (0)) are the determinant and trace of Q j 0 and P j 0 , respectively (block 28)
  • whitening is performed (block 30).
  • the value of ⁇ is chosen to get the best trade-off between the interference cancellation and receiver sensitivity.
  • the whitening filter B j 0 [ B j 0 (0) B j 0 (1)...B j 0 ( K )] is then convolved with the original training sequence to obtain a whitened training sequence P i which is used in the subsequent least squares channel estimator (block 32) in the case that it is determined whitening is needed (bock 28).
  • the whitened signal r w ( n ) exhibits sufficient signal quality (e.g ., C/I) and the noise spectrum is sufficiently whitened (block 31)
  • a least squares estimation is performed (block 32). Otherwise, another pass through the correlation channel estimator (block 24) and whitening filter (block 26) is performed.
  • the received signal may be iteratively processed by the correlation channel estimator and whitening filter multiple times, until the least squares based channel estimation can be advantageously applied or the maximum number of executions has been reached.
  • the whitened signal r w ( n ) and channel estimates h LS - w are forwarded to the demodulator 16.
  • the least squares channel estimation (block 32) may be iteratively performed until signal quality and/or noise spectrum meet predetermined thresholds (this iteration not depicted in Fig. 2 ).
  • the original received signal r ( n ) and channel estimates h LS are forwarded to the demodulator 16.
  • the least squares channel estimation process based on the original training sequence (block 36) is performed only once.
  • the noise metrics considered at block 28 may comprise measures of noise power or signal quality, such as the Signal to Noise and Interference Ratio (SNIR) or Carrier to Interference ratio (C/I). For example, a low C/I would direct flow from block 28 to block 30, whereas a high C/I would direct flow to block 34.
  • assessing the noise metrics at block 28 may comprise determining whether the noise is white or colored, such as inspecting the value of off-diagonal elements of an impairment covariance matrix. White noise would direct flow from block 28 to block 34 whereas colored noise would direct flow to block 30.
  • a combination of noise power and noise color may be used. In general, those of skill in the art may readily implement decision variables appropriate for particular implementations, given the teachings of the present disclosure.
  • Figure 2 depicts only one "pass" through the hybrid channel estimator 14, those of skill in the art will recognize that, e.g., the correlation-based channel estimation and interference suppression filtering ( i . e ., blocks 24-26) maybe iteratively performed two or more times, to successively improve signal quality by suppressing interference, prior to assessing noise color to select the type of least squares channel estimation to employ.
  • the least squares channel estimation can also be performed a number of times to suppress more interference depending on the noise metrics calculated in the decision function. Note the decision function is in general performed several times, but the decision to switch to the least squares channel estimation from correlation channel estimation is a critical one with regard to receiver performance.
  • Figure 3 depicts the frame error rate (FER) performance for CS4 used in GPRS. It can be seen that at the high C/I (e.g., 20 dB or higher), using the correlation channel estimation in both stages (CR-CR) has the worst performance due to the channel estimation bias. Using the least squares channel estimation in both stages (LS-LS) is more than 2 dB better than CR-CR at 10% FER. However, the hybrid method 20 of using the correlation channel estimation in the first stage and the least squares channel estimation in the second stage (CR-WLS) has the best performance; it is 3.5 dB better than correlation alone and 0.5 dB better than least squares alone.
  • FER frame error rate
  • Figure 4 depicts the FER of a GSM full-rate speech (FS) channel which operates at very low C/I of around 1.0 dB. It can be seen that using only least squares channel estimation (LS-LS) has the worst performance due to least squares channel estimation distortion at low C/I.
  • the correlation based method in both stages (CR-CR) is 1.5 dB better than least squares alone.
  • the hybrid method 20 of using the correlation channel estimation in the first stage and the least squares channel estimation in the second stage (CR-WLS) has the best performance; it is 1.8 dB better than least squares alone and 0.3 dB better than correlation alone.
  • Other simulations over a variety of logical channels, C/I ranges, and fading channel profiles have also verified improved performance of the hybrid method 20 as compared to the prior art.

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Noise Elimination (AREA)
  • Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)
EP10810977.8A 2009-12-18 2010-12-15 Hybrid channel estimation using correlation techniques and least squares Active EP2514155B1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US12/641,450 US8175200B2 (en) 2009-12-18 2009-12-18 Hybrid correlation and least squares channel estimation
PCT/IB2010/055822 WO2011073915A2 (en) 2009-12-18 2010-12-15 Hybrid correlation and least squares channel estimation

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EP2514155A2 EP2514155A2 (en) 2012-10-24
EP2514155B1 true EP2514155B1 (en) 2017-04-26

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JP5539533B2 (ja) 2014-07-02
WO2011073915A2 (en) 2011-06-23
EP2514155A2 (en) 2012-10-24
US8175200B2 (en) 2012-05-08
JP2013514707A (ja) 2013-04-25
US20110150154A1 (en) 2011-06-23
WO2011073915A3 (en) 2011-10-13

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